Generate Tie Points

Feature Description

Tie points are homologous image points that can construct stereo models or establish connection relationships between adjacent models (images). Generating tie points helps improve accuracy during geometric correction to ensure spatial consistency of imagery.

SuperMap ImageX Pro 11i(2023) version and later support this feature.

Feature Entry

DOM Automated Processing Flow/DSM Process Window -> Generate Tie Points Node.

Parameter Description

Parameter Description Type
Dataset

Displays the dataset containing imagery for tie point generation (non-editable).

 DatasetMosaic
Input Image Type Select image type for tie point generation. Default: Panchromatic Image. Options include Multispectral Image, Forward-Looking Image, Rear-View Image, or Front View and Rear View Image. ComboBoxImageType
Reference Adjustment File

Use existing adjustment file information to align newly generated tie points with established accuracy standards. Manage multiple reference files using Add/Delete toolbar buttons.

Reference files are obtained through Block Adjustment.

 ReferenceFileData
Error Threshold

Coarse error elimination threshold for image matching. Range: [0,40], Default: 5px.

During matching, least squares fitting removes points exceeding threshold. Higher values retain more points but increase error risk.

 Double
Point Distribution Method

Select distribution pattern: Conventional (default) or Uniform.

  • Conventional: Divides overlap areas into N*M sub-regions, selects n image blocks (512*512) per sub-region for stable matching. Maximizes point coverage.
  • Uniform: Generates evenly distributed points with fewer quantities. Suitable for images with significant internal distortion.
 PointDistributionMethod
Density

Available when Point Distribution Method = Conventional.

Set generation density: Sparse, Medium (default), or Dense. Higher density increases processing time.

 ImageMatchPointDensityLevel
Matching Method

Available when Point Distribution Method = Conventional.

Options: MOTIF (default), AFHORP, RIFT, SIFT, DEEPFT. AFHORP/RIFT support multimodal data. DEEPFT requires AI models and CUDA.

  • MOTIF: A template matching algorithm for multimodal images, featuring lightweight feature descriptors. MOTIF overcomes nonlinear radiometric distortions caused by differences between SAR and optical images.
  • AFHORP: A feature matching algorithm for multimodal images. AFHORP is highly resistant to radiometric distortions and contrast differences in multimodal images, excelling at solving directional reversal and abrupt phase extremum changes.
  • RIFT: A feature matching algorithm robust against large-scale nonlinear radiometric distortions. RIFT enhances feature detection stability and overcomes limitations of gradient information in feature description.
  • SIFT: A method for extracting unique invariant features from images, enabling reliable matching of objects or scenes across different viewpoints.
  • DEEPFT: An image matching method based on deep learning.
 ImageMatchMethod
Max Points per Block

Available when Point Distribution Method = Conventional.

Maximum retained points per image block. Range: [25,2048], Default: 256.

 Integer
Seed Point Quantity

Available when Point Distribution Method = Uniform.

Set seed points per image. Range: [64,6400], Default: 512. Increase for low-texture images.

 Integer
Seed Point Search

Available when Point Distribution Method = Uniform.

Search methods: Corner Points (feature-rich points) or Grid Center Points (default, random selection).

 SearchSeedPointMethod
Template Size

Available when Point Distribution Method = Uniform.

Seed point spacing interval. Range: [1,256], Default: 40px. Larger templates increase reliability and processing time.

 Integer
Search Radius

Available when Point Distribution Method = Uniform.

Seed point search radius. Range: [0,256], Default: 40px. Larger radii expand matching scope and processing time.

 Double
Semantic Culling of Non-Ground Points Disabled by default. When enabled, automatically removes tie points in cloud/building areas using AI semantic analysis. Boolean
Cloud Area Available when Semantic Culling of Non-Ground Points is enabled. Default: Enabled. Uses specified dataset to remove cloud area points. Boolean
Dataset

Displayed when Cloud Area is selected (non-editable).

For DOM workflows: Uses Set Image Path cloud data.

For DSM workflows: Uses Set Image Path (DSM/DEM) cloud data.

 DatasetVector
Building Area

Available when Semantic Culling of Non-Ground Points is enabled.

Default: Enabled. Automatically identifies and removes building area points.

 Boolean

Output

Generates TiePoint vector point dataset in Control Point datasource.

Related Topics

Set Image Path

Set Image Path (DSM/DEM)

Generate Ground Control Points

Block Adjustment